QoS-SLA-Aware Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing in Internet of Vehicles

TL;DR

This paper introduces a novel AI-driven adaptive genetic algorithm for multi-request offloading in edge-cloud vehicular networks, significantly improving execution speed and SLA compliance by considering dynamic vehicle speeds and overlapping requests.

Contribution

It presents a new QoS-SLA-aware adaptive genetic algorithm that optimizes multi-request offloading in heterogeneous edge-cloud systems for vehicles, accounting for dynamic conditions and SLA constraints.

Findings

The proposed algorithm executes requests 1.22 times faster than random offloading.

It reduces SLA violations by 59.9% compared to random offloading.

It outperforms baseline genetic approaches with 1.14 times better performance and fewer violations.

Abstract

The Internet of Vehicles over Vehicular Ad-hoc Networks is an emerging technology enabling the development of smart city applications focused on improving traffic safety, traffic efficiency, and the overall driving experience. These applications have stringent requirements detailed in Service Level Agreement. Since vehicles have limited computational and storage capabilities, applications requests are offloaded onto an integrated edge-cloud computing system. Existing offloading solutions focus on optimizing the application's Quality of Service (QoS) in terms of execution time, and respecting a single SLA constraint. They do not consider the impact of overlapped multi-requests processing nor the vehicle's varying speed. This paper proposes a novel Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA) to optimize the application's execution time for multi-request offloading in a heterogeneous edge-cloud computing system, which considers the impact of processing multi-requests overlapping and dynamic vehicle speed. The proposed genetic algorithm integrates an adaptive penalty function to assimilate the SLA constraints regarding latency, processing time, deadline, CPU, and memory requirements. Numerical experiments and analysis compare our QoS-SLA-AGA to random offloading, and baseline genetic-based approaches. Results show QoS-SLA-AGA executes the requests 1.22 times faster on average compared to the random offloading approach and with 59.9% fewer SLA violations. In contrast, the baseline genetic-based approach increases the requests' performance by 1.14 times, with 19.8% more SLA violations.